To solve the poor performance of the single-server polling system in high traffic and the complex analysis of the multi-server polling system, a synchronous double-server polling system is proposed, and its performance is analyzed using a Backpropagation (BP) neural network prediction algorithm. Experimental data are processed and analyzed, and a three-layer multiinput single-output BP network model is constructed to predict the performance of the polling system under different arrival rates of information packets. In the prediction stage, first, the data are processed and the average queue length under different information arrival rates is used to form a sequence. Subsequently, a multiinput single-output BP neural network is constructed for prediction. Experimental results show that the algorithm can accurately predict the performance of the double-server polling system, thereby facilitating research regarding polling systems.